{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab we're going to do simple data exploration using Pandas. Our objective is to learn basic operations that aid in visual data exploration.\n",
    "\n",
    "<br>\n",
    "\n",
    "First, let's import our required libraries and read in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#loansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')\n",
    "#/Users/briand/Desktop/ds course/datasets\n",
    "\n",
    "loansData = pd.read_csv('/Users/briand/Desktop/ds course/datasets/loansData.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full schema can be found <a href=\"https://github.com/herrfz/dataanalysis/blob/master/assignment1/Assignment1.pdf\">here</a>, but let's use native Pandas methods to also explore the data. Although not specified above, this csv has row headers and the read_csv function implicitly knows this. <br>\n",
    "\n",
    "Let's take a look at the column names, in a nicely readable way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Names Are:\n",
      "\n",
      "CustNUm\n",
      "Amount.Requested\n",
      "Amount.Funded.By.Investors\n",
      "Interest.Rate\n",
      "Loan.Length\n",
      "Loan.Purpose\n",
      "Debt.To.Income.Ratio\n",
      "State\n",
      "Home.Ownership\n",
      "Monthly.Income\n",
      "FICO.Range\n",
      "Open.CREDIT.Lines\n",
      "Revolving.CREDIT.Balance\n",
      "Inquiries.in.the.Last.6.Months\n",
      "Employment.Length\n",
      "\n",
      "Total # of Columns = 15\n"
     ]
    }
   ],
   "source": [
    "print \"Column Names Are:\"\n",
    "print \"\"\n",
    "\n",
    "for column_name in loansData.columns.values:\n",
    "    print column_name\n",
    "\n",
    "print \"\"\n",
    "print 'Total # of Columns = {}'.format(len(loansData.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is oftentimes helpful to take a quick glance at the first few records (when possible). This is an easy way to discover basic data flaws (i.e., all nulls, misaligned fields, etc.). We can do this in Pandas very easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustNUm</th>\n",
       "      <td>              81174</td>\n",
       "      <td>              99592</td>\n",
       "      <td>              80059</td>\n",
       "      <td>              15825</td>\n",
       "      <td>       33182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount.Requested</th>\n",
       "      <td>              20000</td>\n",
       "      <td>              19200</td>\n",
       "      <td>              35000</td>\n",
       "      <td>              10000</td>\n",
       "      <td>       12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount.Funded.By.Investors</th>\n",
       "      <td>              20000</td>\n",
       "      <td>              19200</td>\n",
       "      <td>              35000</td>\n",
       "      <td>               9975</td>\n",
       "      <td>       12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Interest.Rate</th>\n",
       "      <td>              8.90%</td>\n",
       "      <td>             12.12%</td>\n",
       "      <td>             21.98%</td>\n",
       "      <td>              9.99%</td>\n",
       "      <td>      11.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan.Length</th>\n",
       "      <td>          36 months</td>\n",
       "      <td>          36 months</td>\n",
       "      <td>          60 months</td>\n",
       "      <td>          36 months</td>\n",
       "      <td>   36 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan.Purpose</th>\n",
       "      <td> debt_consolidation</td>\n",
       "      <td> debt_consolidation</td>\n",
       "      <td> debt_consolidation</td>\n",
       "      <td> debt_consolidation</td>\n",
       "      <td> credit_card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debt.To.Income.Ratio</th>\n",
       "      <td>             14.90%</td>\n",
       "      <td>             28.36%</td>\n",
       "      <td>             23.81%</td>\n",
       "      <td>             14.30%</td>\n",
       "      <td>      18.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <td>                 SC</td>\n",
       "      <td>                 TX</td>\n",
       "      <td>                 CA</td>\n",
       "      <td>                 KS</td>\n",
       "      <td>          NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Home.Ownership</th>\n",
       "      <td>           MORTGAGE</td>\n",
       "      <td>           MORTGAGE</td>\n",
       "      <td>           MORTGAGE</td>\n",
       "      <td>           MORTGAGE</td>\n",
       "      <td>        RENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly.Income</th>\n",
       "      <td>            6541.67</td>\n",
       "      <td>            4583.33</td>\n",
       "      <td>              11500</td>\n",
       "      <td>            3833.33</td>\n",
       "      <td>        3195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FICO.Range</th>\n",
       "      <td>            735-739</td>\n",
       "      <td>            715-719</td>\n",
       "      <td>            690-694</td>\n",
       "      <td>            695-699</td>\n",
       "      <td>     695-699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open.CREDIT.Lines</th>\n",
       "      <td>                 14</td>\n",
       "      <td>                 12</td>\n",
       "      <td>                 14</td>\n",
       "      <td>                 10</td>\n",
       "      <td>          11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revolving.CREDIT.Balance</th>\n",
       "      <td>              14272</td>\n",
       "      <td>              11140</td>\n",
       "      <td>              21977</td>\n",
       "      <td>               9346</td>\n",
       "      <td>       14469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inquiries.in.the.Last.6.Months</th>\n",
       "      <td>                  2</td>\n",
       "      <td>                  1</td>\n",
       "      <td>                  1</td>\n",
       "      <td>                  0</td>\n",
       "      <td>           0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employment.Length</th>\n",
       "      <td>           &lt; 1 year</td>\n",
       "      <td>            2 years</td>\n",
       "      <td>            2 years</td>\n",
       "      <td>            5 years</td>\n",
       "      <td>     9 years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 0                   1                   2                   3            4\n",
       "CustNUm                                      81174               99592               80059               15825        33182\n",
       "Amount.Requested                             20000               19200               35000               10000        12000\n",
       "Amount.Funded.By.Investors                   20000               19200               35000                9975        12000\n",
       "Interest.Rate                                8.90%              12.12%              21.98%               9.99%       11.71%\n",
       "Loan.Length                              36 months           36 months           60 months           36 months    36 months\n",
       "Loan.Purpose                    debt_consolidation  debt_consolidation  debt_consolidation  debt_consolidation  credit_card\n",
       "Debt.To.Income.Ratio                        14.90%              28.36%              23.81%              14.30%       18.78%\n",
       "State                                           SC                  TX                  CA                  KS           NJ\n",
       "Home.Ownership                            MORTGAGE            MORTGAGE            MORTGAGE            MORTGAGE         RENT\n",
       "Monthly.Income                             6541.67             4583.33               11500             3833.33         3195\n",
       "FICO.Range                                 735-739             715-719             690-694             695-699      695-699\n",
       "Open.CREDIT.Lines                               14                  12                  14                  10           11\n",
       "Revolving.CREDIT.Balance                     14272               11140               21977                9346        14469\n",
       "Inquiries.in.the.Last.6.Months                   2                   1                   1                   0            0\n",
       "Employment.Length                         < 1 year             2 years             2 years             5 years      9 years"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData.head().transpose() #We transpose it so it will fit in the display window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's run a quick line to get summary statistics of the numeric fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustNUm</th>\n",
       "      <td> 2500</td>\n",
       "      <td> 51631.597200</td>\n",
       "      <td> 30053.345508</td>\n",
       "      <td>   10.00</td>\n",
       "      <td> 26523.00</td>\n",
       "      <td> 50653</td>\n",
       "      <td> 77580.50</td>\n",
       "      <td> 104202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount.Requested</th>\n",
       "      <td> 2500</td>\n",
       "      <td> 12406.500000</td>\n",
       "      <td>  7801.544872</td>\n",
       "      <td> 1000.00</td>\n",
       "      <td>  6000.00</td>\n",
       "      <td> 10000</td>\n",
       "      <td> 17000.00</td>\n",
       "      <td>  35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount.Funded.By.Investors</th>\n",
       "      <td> 2500</td>\n",
       "      <td> 12001.573236</td>\n",
       "      <td>  7745.320754</td>\n",
       "      <td>   -0.01</td>\n",
       "      <td>  6000.00</td>\n",
       "      <td> 10000</td>\n",
       "      <td> 16000.00</td>\n",
       "      <td>  35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly.Income</th>\n",
       "      <td> 2499</td>\n",
       "      <td>  5688.931321</td>\n",
       "      <td>  3963.118185</td>\n",
       "      <td>  588.50</td>\n",
       "      <td>  3500.00</td>\n",
       "      <td>  5000</td>\n",
       "      <td>  6800.00</td>\n",
       "      <td> 102750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open.CREDIT.Lines</th>\n",
       "      <td> 2498</td>\n",
       "      <td>    10.075661</td>\n",
       "      <td>     4.508644</td>\n",
       "      <td>    2.00</td>\n",
       "      <td>     7.00</td>\n",
       "      <td>     9</td>\n",
       "      <td>    13.00</td>\n",
       "      <td>     38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revolving.CREDIT.Balance</th>\n",
       "      <td> 2498</td>\n",
       "      <td> 15244.559648</td>\n",
       "      <td> 18308.549795</td>\n",
       "      <td>    0.00</td>\n",
       "      <td>  5585.75</td>\n",
       "      <td> 10962</td>\n",
       "      <td> 18888.75</td>\n",
       "      <td> 270800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inquiries.in.the.Last.6.Months</th>\n",
       "      <td> 2498</td>\n",
       "      <td>     0.906325</td>\n",
       "      <td>     1.231036</td>\n",
       "      <td>    0.00</td>\n",
       "      <td>     0.00</td>\n",
       "      <td>     0</td>\n",
       "      <td>     1.00</td>\n",
       "      <td>      9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                count          mean           std      min       25%    50%       75%     max\n",
       "CustNUm                          2500  51631.597200  30053.345508    10.00  26523.00  50653  77580.50  104202\n",
       "Amount.Requested                 2500  12406.500000   7801.544872  1000.00   6000.00  10000  17000.00   35000\n",
       "Amount.Funded.By.Investors       2500  12001.573236   7745.320754    -0.01   6000.00  10000  16000.00   35000\n",
       "Monthly.Income                   2499   5688.931321   3963.118185   588.50   3500.00   5000   6800.00  102750\n",
       "Open.CREDIT.Lines                2498     10.075661      4.508644     2.00      7.00      9     13.00      38\n",
       "Revolving.CREDIT.Balance         2498  15244.559648  18308.549795     0.00   5585.75  10962  18888.75  270800\n",
       "Inquiries.in.the.Last.6.Months   2498      0.906325      1.231036     0.00      0.00      0      1.00       9"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now focus on a single column, 'Monthly.Income.' First things first, let's display the first five records of just this field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6541.67\n",
       "1     4583.33\n",
       "2    11500.00\n",
       "3     3833.33\n",
       "4     3195.00\n",
       "Name: Monthly.Income, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Student input line here\n",
    "loansData['Monthly.Income'][0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try and understand the distribution of this field. We can do this using the hist() method and matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AsxQdwFmKDuAoRQdwlqID1E7FQkREWqlYdMuiAziz6ADOLDqAI4sO4MyiA9ROxUJERFrN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\niIiIiIiIHHn+PwLPLUhvS9+9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eecaa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "hist_inc = loansData['Monthly.Income'].hist()\n",
    "plt.title('Histogram of Monthly Income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How would you characterize the shape of this distribution? Is there anything we can do to the income variable to make the distribution more bell curved? Let's create a new column in the dataframe called 'Monthly.LogIncome' and print a histogram of it. What might be some advantages of making such a transformation?\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EREREREREREREREREREREREREREbd/wP3vdON9HnxKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10723c510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Student input line here\n",
    "loansData['Monthly.LogIncome'] = np.log(loansData['Monthly.Income']) \n",
    "plt.figure()\n",
    "h = loansData['Monthly.LogIncome'].hist()\n",
    "plt.title('Histogram of Log(Monthly Income)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Now let's answer some questions about the data.\n",
    "\n",
    "<b>Q1: What is the cardinality (i.e., # of distinct values) for 'Interest.Rate' and 'FICO.Range'</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cardinality of Interest.Rate = 275\n",
      "Cardinality of FICO.Range = 38\n"
     ]
    }
   ],
   "source": [
    "#Student input code here\n",
    "\n",
    "print 'Cardinality of Interest.Rate = {}'.format(loansData['Interest.Rate'].describe()['unique'])\n",
    "print 'Cardinality of FICO.Range = {}'.format(loansData['FICO.Range'].describe()['unique'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Q2: What data type did Pandas set fo Interest.Rate? Can we create a new field which is stored as a float?</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Student input code here - show the field type\n",
    "type(loansData['Interest.Rate'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, create a new field 'Interest.Rate.Num' where 'Interest.Rate' is converted to a float. Hint: this can be done in one line using the .str assessor, strip and astype methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Student input code here - convert field to a float using the '.str' assessor\n",
    "loansData['Interest.Rate.Num'] = loansData['Interest.Rate'].str.strip('%').astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Q3: Can we get a sense of the relationship between monthly income and interest rate? Use the scatter() function from Matplotlib.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10dd280d0>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5mWVkYxX6g0QTwl4VONWUwghTDAPMFBQ2QR8TFdKPm2AvMwVRKvAPgQMDx0YF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\nFfY59fMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ee51f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Student input line here\n",
    "plt.scatter(loansData['Monthly.Income'], loansData['Interest.Rate.Num'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the skewed distribution of Income, is this chart visually helpful? Let's try the Log of income instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10f32fdd0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p3AGhbt2mRctoL8xDTREiSVM9k6Gm1PYnTc2DB5qiRIIiVy0GA1sUohg0Zmhz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SNGjAGMmMRjxIgRYwAjJvEYMWLEGMCISTxGjBgxBjBiEo8RI0aMAYyYxGPEiBFjACMm8RgxYsQY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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dba5a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Student input code here\n",
    "plt.scatter(loansData['Monthly.LogIncome'], loansData['Interest.Rate.Num'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Q4: What is the average interest rate for each FICO range?</b> <br>\n",
    "Hint: use the groupby() method in Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Student input code here\n",
    "fico_grp = loansData[['Interest.Rate.Num', 'FICO.Range']].groupby(['FICO.Range']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interest.Rate.Num</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FICO.Range</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>640-644</th>\n",
       "      <td> 15.212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645-649</th>\n",
       "      <td> 14.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650-654</th>\n",
       "      <td> 15.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655-659</th>\n",
       "      <td> 14.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660-664</th>\n",
       "      <td> 18.492480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665-669</th>\n",
       "      <td> 17.448069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670-674</th>\n",
       "      <td> 16.248480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675-679</th>\n",
       "      <td> 15.854699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680-684</th>\n",
       "      <td> 15.126688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685-689</th>\n",
       "      <td> 14.654855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690-694</th>\n",
       "      <td> 14.732071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695-699</th>\n",
       "      <td> 14.146863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700-704</th>\n",
       "      <td> 13.356870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705-709</th>\n",
       "      <td> 12.659851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710-714</th>\n",
       "      <td> 12.433125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715-719</th>\n",
       "      <td> 11.181935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720-724</th>\n",
       "      <td> 11.035789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725-729</th>\n",
       "      <td> 10.652979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730-734</th>\n",
       "      <td>  9.956170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735-739</th>\n",
       "      <td>  9.623538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740-744</th>\n",
       "      <td>  9.592264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745-749</th>\n",
       "      <td>  9.901667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750-754</th>\n",
       "      <td>  8.467705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755-759</th>\n",
       "      <td>  8.996087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760-764</th>\n",
       "      <td>  8.627609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765-769</th>\n",
       "      <td>  7.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770-774</th>\n",
       "      <td>  6.752941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775-779</th>\n",
       "      <td>  8.744091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780-784</th>\n",
       "      <td>  7.589286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785-789</th>\n",
       "      <td>  8.497895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790-794</th>\n",
       "      <td>  7.560500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795-799</th>\n",
       "      <td>  8.391538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800-804</th>\n",
       "      <td>  7.638462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805-809</th>\n",
       "      <td>  7.426364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810-814</th>\n",
       "      <td>  8.293750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815-819</th>\n",
       "      <td>  6.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820-824</th>\n",
       "      <td>  7.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830-834</th>\n",
       "      <td>  7.620000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Interest.Rate.Num\n",
       "FICO.Range                   \n",
       "640-644             15.212000\n",
       "645-649             14.883333\n",
       "650-654             15.130000\n",
       "655-659             14.930000\n",
       "660-664             18.492480\n",
       "665-669             17.448069\n",
       "670-674             16.248480\n",
       "675-679             15.854699\n",
       "680-684             15.126688\n",
       "685-689             14.654855\n",
       "690-694             14.732071\n",
       "695-699             14.146863\n",
       "700-704             13.356870\n",
       "705-709             12.659851\n",
       "710-714             12.433125\n",
       "715-719             11.181935\n",
       "720-724             11.035789\n",
       "725-729             10.652979\n",
       "730-734              9.956170\n",
       "735-739              9.623538\n",
       "740-744              9.592264\n",
       "745-749              9.901667\n",
       "750-754              8.467705\n",
       "755-759              8.996087\n",
       "760-764              8.627609\n",
       "765-769              7.783333\n",
       "770-774              6.752941\n",
       "775-779              8.744091\n",
       "780-784              7.589286\n",
       "785-789              8.497895\n",
       "790-794              7.560500\n",
       "795-799              8.391538\n",
       "800-804              7.638462\n",
       "805-809              7.426364\n",
       "810-814              8.293750\n",
       "815-819              6.925000\n",
       "820-824              7.900000\n",
       "830-834              7.620000"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
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